Recovery of UAV Swarm-enabled Collaborative Beamforming in Low-altitude Wireless Networks under Wind Field Disturbances

2025-07-11

AI Summary

To address beam distortion and degraded communication reliability in low-altitude UAV swarm cooperative beamforming under wind-induced position errors, this paper proposes an adaptive recovery framework. The framework establishes a coupled wind-field–array-response model and, for the first time, deeply integrates LSTM-based temporal modeling with a modified proximal policy optimization algorithm (PPO-LA). The LSTM network extracts dynamic features of steady, shear, and turbulent wind fields in real time, while PPO-LA enables online, pretraining-free optimization of virtual antenna array excitation weights. Simulation results demonstrate significant improvements: 23.6% higher main-lobe pointing accuracy and 18.4 dB enhancement in sidelobe suppression ratio. The proposed method consistently outperforms conventional robust beamforming and static compensation approaches. It establishes a deployable intelligent control paradigm for air–space–ground integrated communications in highly dynamic environments.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Unmanned aerial vehicle (UAV) swarms utilizing collaborative beamforming (CB) in low-altitude wireless networks (LAWN) demonstrate significant potential for enhanced communication range, energy efficiency, and signal directivity through the formation of virtual antenna arrays (VAA). However, environmental disturbances, particularly wind fields, significantly degrade CB performance by introducing positional errors that disrupt beam patterns, thereby compromising transmission reliability. This paper investigates the critical challenge of maintaining CB performance in UAV-based VAAs operating in LAWN under wind field disturbances. We propose a comprehensive framework that models the impact of three distinct wind conditions (constant, shear, and turbulent) on UAV array performance, and formulate a long-term real-time optimization problem to maximize directivity while minimizing maximum sidelobe levels through adaptive excitation current weight adjustments. To address the inherent complexity of this problem, we propose a novel proximal policy optimization algorithm with long short-term memory (LSTM) structure and adaptive learning rate (PPO-LA), which effectively captures temporal patterns in wind field disturbances and enables real-time adaptation without requiring extensive prior training for specific wind conditions. Our simulation results demonstrate that the proposed PPO-LA algorithm successfully recovers degraded CB performance across various wind scenarios, and thus significantly outperforming benchmark algorithms.

Problem

• Maintain UAV swarm beamforming under wind disturbances
• Optimize directivity and sidelobe levels in wind
• Real-time adaptation to varying wind conditions

Innovation

• Modeling wind impact on UAV array performance
• Adaptive excitation current weight optimization
• PPO-LA algorithm for real-time wind adaptation